CJ Carey
Software Engineer at Google
New York, New York, United States
Summary
🤩
Rockstar🎓
Top SchoolCJ Carey is a software engineer with 15 years of experience blending research-grade machine learning and production systems engineering, currently at Google in New York. He holds a PhD in Machine Learning and an MS in Computer Science and has a strong open-source footprint across flagship scientific Python projects like SciPy, NumPy, scikit-learn and Matplotlib, where he optimized sparse matrix operations, improved 3D plotting performance, and fixed critical numerical edge cases. His contributions span backend and full-stack work—including BrowserFS—and demonstrate an ability to translate mathematical and MATLAB-era code into robust Python implementations. Known for pragmatic bug fixes, performance-focused refactors, and compatibility-minded engineering, he brings both deep numerical insight and production-hardened software craft to complex data and ML infrastructure.
15 years of coding experience
7 years of employment as a software developer
University of Massachusetts AmherstMaster of Science (MS), Computer Science, Master of Science (MS), Computer Science at Washington University in St. Louis
English
Stackoverflow
17,477reputation
1.6mreached
165answers
36questions
numpy
top-1%
scipy
top-5%
ruby
top-5%
python
top-1%
macos
top-5%
cython
top-5%
Github Skills (46)
algorithm10
data-visualizations10
algorithms10
scipy10
javascript10
coffeescript10
python10
back-end-development10
data-science10
matplotlib10
testing10
scikit10
metric-learning10
sparse-matrix10
machine-learning10
Programming languages (13)
Github contributions (5)
jvilk/BrowserFS
May 2013 - Jul 2015
BrowserFS is an in-browser filesystem that emulates the Node JS filesystem API and supports storing and retrieving files from various backends.
Role in this project:
Full-stack Developer
Full-stack DeveloperContributions:112 commits, 1 push, 4 comments in 2 years 2 months
Contributions summary:CJ primarily worked on the BrowserFS project, implementing and fixing various features. They addressed issues related to setImmediate polyfills and root directory handling. The user refactored and corrected references within the code, fixed logic for file writeability and added/modified file stats. Several tests were also updated and converted to require the node polyfill, alongside adding new tests.
apibrowsernode-jsjavascriptbackends
scikit-learn-contrib/metric-learn
Nov 2013 - Jun 2022
Metric learning algorithms in Python
Role in this project:ML Engineer
ML EngineerContributions:6 releases, 18 reviews, 103 commits in 8 years 9 months
Contributions summary:CJ's commits primarily focused on improving and maintaining metric learning algorithms within the `metric-learn` repository. They addressed compatibility issues with older versions of NumPy and avoided the use of einsum to ensure wider compatibility. Further work included fixing various LMNN issues and updating the interface for ITML. Other commits include the addition of a setup.py file, moving tests out, and converting to a standard sklearn convention.
pythonmetric-learningmachine-learninglearning-algorithmsscikit-learn
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